Image Enhancement Method in Underground Coal Mines Based on an Improved Particle Swarm Optimization Algorithm

Abstract

Due to the poor lighting conditions and the presence of a large amount of suspended dust in coal mines, obtained video has problems with uneven lighting and low differentiation of facial features. In order to address these problems, an improved image enhancement method is proposed. Firstly, the characteristics of underground coal mine images are analyzed, and median filtering is selected for noise removal. Then, the gamma function and fractional order operator are introduced, and an image enhancement algorithm based on particle swarm optimization is proposed. Finally, several experiments are conducted, and the results show that the proposed improved algorithm outperforms classical image enhancement algorithms, such as MSR, CLAHE and HF. Compared with the original image, the evaluation metrics of the enhanced Yale face images, including average local standard deviation, average gradient, information entropy and contrast, are improved by 113.1%, 63.8%, 22.8% and 24.1%, respectively. Moreover, the proposed algorithm achieves a superior enhancement effect in the simulated coal mine environment.

TMCD: Threshold multi-channel prior-based defogging algorithm for underground coal mine images

“…Therefore, a common practice is to consider the brightest color in the image as the global atmospheric light value. However, in certain scenes, objects may exhibit brightness levels that exceed the atmospheric light value, such as light sources in environments like underground mines 20 . Failing to account for this scenario may lead to incorrect estimations, which in turn affects the accuracy of the transmission rate estimation.…”

“…Moreover, some algorithms may introduce distortions or artifacts during the dehazing process, compromising the accuracy and reliability of the images 19 . These issues present significant challenges to the safety monitoring and production efficiency in coal mining environments 20 .…”

A Foreign Object Detection Method for Belt Conveyors Based on an Improved YOLOX Model

“…Intelligent detection for the production of safe underground coal mines has become a hot research topic [5,6]. By utilizing video surveillance images, combined with image processing and machine-vision-related technologies, the theory of mine image monitoring has been applied to multiple aspects of automatic safety detection in coal mines, such as the automatic identification of spontaneous combustion fires [7], coal production monitoring [8], face detection and recognition methods for underground miners [9], and the automatic recognition of coal rock interfaces in coal faces [10]. However, traditional belt conveyor foreign object systems still rely on cameras to transmit collected video data to the central control room, where the staff can monitor the coal transportation area and the surrounding environment in real time.…”